Overview

Dataset statistics

Number of variables31
Number of observations257908
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.6 MiB
Average record size in memory559.4 B

Variable types

Numeric18
Categorical11
Unsupported1
DateTime1

Alerts

NOMBRE MATERIA has a high cardinality: 1108 distinct values High cardinality
df_index is highly correlated with EXPEDIENTE and 4 other fieldsHigh correlation
EXPEDIENTE is highly correlated with df_index and 3 other fieldsHigh correlation
INICIO is highly correlated with df_index and 3 other fieldsHigh correlation
ULTIMO CICLO is highly correlated with df_index and 3 other fieldsHigh correlation
HORAS TEORIA is highly correlated with HORAS LABORATORIOHigh correlation
HORAS LABORATORIO is highly correlated with HORAS TEORIAHigh correlation
NUMERO INSCRIPCIONES is highly correlated with NUMERO REPHigh correlation
NUMERO REP is highly correlated with NUMERO INSCRIPCIONESHigh correlation
CICLO MATERIA is highly correlated with df_index and 3 other fieldsHigh correlation
CLAVE MAESTRO is highly correlated with CLAVE MAESTRO2High correlation
CLAVE MAESTRO2 is highly correlated with CLAVE MAESTROHigh correlation
TIPO MOVIMIENTO is highly correlated with df_indexHigh correlation
df_index is highly correlated with CICLO MATERIAHigh correlation
EXPEDIENTE is highly correlated with INICIOHigh correlation
AVANCE is highly correlated with KARDEXHigh correlation
KARDEX is highly correlated with AVANCE and 1 other fieldsHigh correlation
INICIO is highly correlated with EXPEDIENTEHigh correlation
ULTIMO CICLO is highly correlated with KARDEXHigh correlation
HORAS TEORIA is highly correlated with HORAS LABORATORIOHigh correlation
HORAS LABORATORIO is highly correlated with HORAS TEORIAHigh correlation
NUMERO INSCRIPCIONES is highly correlated with NUMERO REPHigh correlation
NUMERO REP is highly correlated with NUMERO INSCRIPCIONESHigh correlation
CICLO MATERIA is highly correlated with df_indexHigh correlation
CLAVE MAESTRO is highly correlated with CLAVE MAESTRO2High correlation
CLAVE MAESTRO2 is highly correlated with CLAVE MAESTROHigh correlation
df_index is highly correlated with EXPEDIENTE and 2 other fieldsHigh correlation
EXPEDIENTE is highly correlated with df_index and 3 other fieldsHigh correlation
INICIO is highly correlated with df_index and 3 other fieldsHigh correlation
ULTIMO CICLO is highly correlated with EXPEDIENTE and 2 other fieldsHigh correlation
HORAS TEORIA is highly correlated with HORAS LABORATORIOHigh correlation
HORAS LABORATORIO is highly correlated with HORAS TEORIAHigh correlation
CICLO MATERIA is highly correlated with df_index and 3 other fieldsHigh correlation
CLAVE MAESTRO is highly correlated with CLAVE MAESTRO2High correlation
CLAVE MAESTRO2 is highly correlated with CLAVE MAESTROHigh correlation
ESTATUS MATERIA is highly correlated with NUMERO REP and 1 other fieldsHigh correlation
NUMERO REP is highly correlated with ESTATUS MATERIAHigh correlation
NUMERO BAJAS is highly correlated with ESTATUS MATERIAHigh correlation
PROGRAMA is highly correlated with NOMBRE DEPARTAMENTO and 2 other fieldsHigh correlation
TIPO is highly correlated with ESTATUSHigh correlation
NOMBRE DEPARTAMENTO is highly correlated with PROGRAMA and 2 other fieldsHigh correlation
CREDITOS PASANTE is highly correlated with PROGRAMA and 2 other fieldsHigh correlation
DIVISION is highly correlated with PROGRAMA and 2 other fieldsHigh correlation
ESTATUS is highly correlated with TIPOHigh correlation
df_index is highly correlated with CREDITOS PASANTE and 5 other fieldsHigh correlation
DIVISION is highly correlated with NOMBRE DEPARTAMENTO and 3 other fieldsHigh correlation
NOMBRE DEPARTAMENTO is highly correlated with DIVISION and 3 other fieldsHigh correlation
EXPEDIENTE is highly correlated with INICIOHigh correlation
PROGRAMA is highly correlated with DIVISION and 3 other fieldsHigh correlation
PLAN is highly correlated with CREDITOS PASANTE and 2 other fieldsHigh correlation
CREDITOS PASANTE is highly correlated with df_index and 9 other fieldsHigh correlation
ESTATUS is highly correlated with df_index and 6 other fieldsHigh correlation
TIPO is highly correlated with ESTATUS and 3 other fieldsHigh correlation
AVANCE is highly correlated with df_index and 7 other fieldsHigh correlation
KARDEX is highly correlated with ESTATUS and 5 other fieldsHigh correlation
PERIODO is highly correlated with CREDITOS PASANTE and 4 other fieldsHigh correlation
INICIO is highly correlated with df_index and 1 other fieldsHigh correlation
ULTIMO CICLO is highly correlated with AVANCE and 1 other fieldsHigh correlation
MATERIA is highly correlated with DIVISION and 6 other fieldsHigh correlation
HORAS TEORIA is highly correlated with MATERIA and 1 other fieldsHigh correlation
HORAS LABORATORIO is highly correlated with MATERIA and 1 other fieldsHigh correlation
ESTATUS MATERIA is highly correlated with CALIFICACION ORDINARIO and 4 other fieldsHigh correlation
CALIFICACION ORDINARIO is highly correlated with KARDEX and 3 other fieldsHigh correlation
NUMERO INSCRIPCIONES is highly correlated with ESTATUS MATERIA and 1 other fieldsHigh correlation
NUMERO REP is highly correlated with ESTATUS MATERIA and 2 other fieldsHigh correlation
NUMERO BAJAS is highly correlated with ESTATUS MATERIAHigh correlation
CICLO MATERIA is highly correlated with df_index and 2 other fieldsHigh correlation
CLAVE MAESTRO is highly correlated with CLAVE MAESTRO2High correlation
CLAVE MAESTRO2 is highly correlated with CLAVE MAESTROHigh correlation
TIPO MOVIMIENTO is highly correlated with df_index and 2 other fieldsHigh correlation
GRUPO is an unsupported type, check if it needs cleaning or further analysis Unsupported
AVANCE has 4036 (1.6%) zeros Zeros
KARDEX has 3870 (1.5%) zeros Zeros
PERIODO has 82563 (32.0%) zeros Zeros
ULTIMO CICLO has 4628 (1.8%) zeros Zeros
HORAS TEORIA has 49422 (19.2%) zeros Zeros
HORAS LABORATORIO has 64815 (25.1%) zeros Zeros
CALIFICACION ORDINARIO has 45729 (17.7%) zeros Zeros
CALIFICACION EXTRAORDINARIO has 251653 (97.6%) zeros Zeros
NUMERO INSCRIPCIONES has 28204 (10.9%) zeros Zeros
CLAVE MAESTRO has 47427 (18.4%) zeros Zeros
CLAVE MAESTRO2 has 31249 (12.1%) zeros Zeros

Reproduction

Analysis started2021-12-13 04:30:29.417840
Analysis finished2021-12-13 04:32:18.107925
Duration1 minute and 48.69 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct218423
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95519.08232
Minimum0
Maximum218422
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:18.214098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6452
Q132270
median89468.5
Q3153945.25
95-th percentile205526.65
Maximum218422
Range218422
Interquartile range (IQR)121675.25

Descriptive statistics

Standard deviation66513.84897
Coefficient of variation (CV)0.6963409546
Kurtosis-1.281073949
Mean95519.08232
Median Absolute Deviation (MAD)59628.5
Skewness0.235896633
Sum2.463513548 × 1010
Variance4424092104
MonotonicityNot monotonic
2021-12-12T21:32:18.363898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307002
 
< 0.1%
374192
 
< 0.1%
251412
 
< 0.1%
271882
 
< 0.1%
210432
 
< 0.1%
230902
 
< 0.1%
169452
 
< 0.1%
189922
 
< 0.1%
364162
 
< 0.1%
394662
 
< 0.1%
Other values (218413)257888
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
2184221
< 0.1%
2184211
< 0.1%
2184201
< 0.1%
2184191
< 0.1%
2184181
< 0.1%
2184171
< 0.1%
2184161
< 0.1%
2184151
< 0.1%
2184141
< 0.1%
2184131
< 0.1%

DIVISION
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.4 KiB
DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS
158543 
DIVISIÓN DE CIENCIAS BIOLÓGICAS Y DE LA SALUD
99365 

Length

Max length49
Median length49
Mean length47.45890783
Min length45

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS
2nd rowDIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS
3rd rowDIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS
4th rowDIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS
5th rowDIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS

Common Values

ValueCountFrequency (%)
DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVAS158543
61.5%
DIVISIÓN DE CIENCIAS BIOLÓGICAS Y DE LA SALUD99365
38.5%

Length

2021-12-12T21:32:18.711094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:18.822482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de357273
20.5%
y257908
14.8%
ciencias257908
14.8%
división257908
14.8%
administrativas158543
9.1%
económicas158543
9.1%
salud99365
 
5.7%
la99365
 
5.7%
biológicas99365
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NOMBRE DEPARTAMENTO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.3 KiB
DEPARTAMENTO DE ADMINISTRACIÓN
158543 
DEPARTAMENTO DE ENFERMERÍA
99365 

Length

Max length30
Median length30
Mean length28.45890783
Min length26

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEPARTAMENTO DE ADMINISTRACIÓN
2nd rowDEPARTAMENTO DE ADMINISTRACIÓN
3rd rowDEPARTAMENTO DE ADMINISTRACIÓN
4th rowDEPARTAMENTO DE ADMINISTRACIÓN
5th rowDEPARTAMENTO DE ADMINISTRACIÓN

Common Values

ValueCountFrequency (%)
DEPARTAMENTO DE ADMINISTRACIÓN158543
61.5%
DEPARTAMENTO DE ENFERMERÍA99365
38.5%

Length

2021-12-12T21:32:18.946601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:19.051933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de257908
33.3%
departamento257908
33.3%
administración158543
20.5%
enfermería99365
 
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EXPEDIENTE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7362
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211256301
Minimum7921124
Maximum221230231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:19.165007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7921124
5-th percentile205202006
Q1210150038
median212218543
Q3216211395
95-th percentile220202517
Maximum221230231
Range213309107
Interquartile range (IQR)6061357

Descriptive statistics

Standard deviation18705347.33
Coefficient of variation (CV)0.08854338185
Kurtosis106.0538233
Mean211256301
Median Absolute Deviation (MAD)3014925
Skewness-10.09647688
Sum5.448469009 × 1013
Variance3.498900188 × 1014
MonotonicityNot monotonic
2021-12-12T21:32:19.338893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211211008176
 
0.1%
211200893154
 
0.1%
210200432146
 
0.1%
210202525135
 
0.1%
213201067133
 
0.1%
212218543131
 
0.1%
210215344129
 
0.1%
210210882128
 
< 0.1%
211209909128
 
< 0.1%
214215821122
 
< 0.1%
Other values (7352)256526
99.5%
ValueCountFrequency (%)
792112421
 
< 0.1%
80205401
 
< 0.1%
82112382
 
< 0.1%
851007025
 
< 0.1%
861009460
< 0.1%
861041819
 
< 0.1%
862263812
 
< 0.1%
86255951
 
< 0.1%
87105255
 
< 0.1%
872038866
< 0.1%
ValueCountFrequency (%)
2212302315
 
< 0.1%
2212302152
 
< 0.1%
2212301891
 
< 0.1%
2212301871
 
< 0.1%
2212301802
 
< 0.1%
2212301782
 
< 0.1%
2212300461
 
< 0.1%
22123003046
< 0.1%
22123002543
< 0.1%
22123002142
< 0.1%

PROGRAMA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.3 KiB
LICENCIATURA EN ADMINISTRACIÓN
158543 
LICENCIATURA EN ENFERMERÍA
99365 

Length

Max length30
Median length30
Mean length28.45890783
Min length26

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLICENCIATURA EN ADMINISTRACIÓN
2nd rowLICENCIATURA EN ADMINISTRACIÓN
3rd rowLICENCIATURA EN ADMINISTRACIÓN
4th rowLICENCIATURA EN ADMINISTRACIÓN
5th rowLICENCIATURA EN ADMINISTRACIÓN

Common Values

ValueCountFrequency (%)
LICENCIATURA EN ADMINISTRACIÓN158543
61.5%
LICENCIATURA EN ENFERMERÍA99365
38.5%

Length

2021-12-12T21:32:19.519417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:19.627373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
en257908
33.3%
licenciatura257908
33.3%
administración158543
20.5%
enfermería99365
 
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLAN
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2051.91984
Minimum842
Maximum2172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:19.715418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum842
5-th percentile2042
Q12042
median2042
Q32052
95-th percentile2172
Maximum2172
Range1330
Interquartile range (IQR)10

Descriptive statistics

Standard deviation110.0709424
Coefficient of variation (CV)0.05364290567
Kurtosis77.38760809
Mean2051.91984
Median Absolute Deviation (MAD)0
Skewness-8.042025333
Sum529206542
Variance12115.61236
MonotonicityNot monotonic
2021-12-12T21:32:19.838535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2042156509
60.7%
205265812
25.5%
217233299
 
12.9%
9921895
 
0.7%
971254
 
0.1%
842139
 
0.1%
ValueCountFrequency (%)
842139
 
0.1%
971254
 
0.1%
9921895
 
0.7%
2042156509
60.7%
205265812
25.5%
217233299
 
12.9%
ValueCountFrequency (%)
217233299
 
12.9%
205265812
25.5%
2042156509
60.7%
9921895
 
0.7%
971254
 
0.1%
842139
 
0.1%

CREDITOS PASANTE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
373
156509 
396
65812 
398
33553 
405
 
1895
447
 
139

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row373
2nd row373
3rd row373
4th row373
5th row373

Common Values

ValueCountFrequency (%)
373156509
60.7%
39665812
25.5%
39833553
 
13.0%
4051895
 
0.7%
447139
 
0.1%

Length

2021-12-12T21:32:19.981365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:20.065445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
373156509
60.7%
39665812
25.5%
39833553
 
13.0%
4051895
 
0.7%
447139
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ESTATUS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
E
124968 
A
71435 
I
24057 
I40
20620 
B74
 
12496
Other values (5)
 
4332

Length

Max length3
Median length1
Mean length1.285574701
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI40
2nd rowI40
3rd rowI40
4th rowI40
5th rowI40

Common Values

ValueCountFrequency (%)
E124968
48.5%
A71435
27.7%
I24057
 
9.3%
I4020620
 
8.0%
B7412496
 
4.8%
B6A2389
 
0.9%
B381224
 
0.5%
S622
 
0.2%
B2962
 
< 0.1%
B6B35
 
< 0.1%

Length

2021-12-12T21:32:20.221389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:20.339180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
e124968
48.5%
a71435
27.7%
i24057
 
9.3%
i4020620
 
8.0%
b7412496
 
4.8%
b6a2389
 
0.9%
b381224
 
0.5%
s622
 
0.2%
b2962
 
< 0.1%
b6b35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
R
184364 
I
73544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R184364
71.5%
I73544
 
28.5%

Length

2021-12-12T21:32:20.547601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:20.629423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
r184364
71.5%
i73544
 
28.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AVANCE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct410
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.5203367
Minimum0
Maximum447
Zeros4036
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:20.750127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q1190
median366
Q3390
95-th percentile396
Maximum447
Range447
Interquartile range (IQR)200

Descriptive statistics

Standard deviation123.9115158
Coefficient of variation (CV)0.4324702294
Kurtosis-0.6384290464
Mean286.5203367
Median Absolute Deviation (MAD)30
Skewness-0.8785767104
Sum73895887
Variance15354.06374
MonotonicityNot monotonic
2021-12-12T21:32:20.922571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37356740
22.0%
39654815
21.3%
3984576
 
1.8%
04036
 
1.6%
3782975
 
1.2%
2831806
 
0.7%
961716
 
0.7%
1871615
 
0.6%
3631605
 
0.6%
941454
 
0.6%
Other values (400)126570
49.1%
ValueCountFrequency (%)
04036
1.6%
145
 
< 0.1%
2289
 
0.1%
3321
 
0.1%
536
 
< 0.1%
6612
 
0.2%
761
 
< 0.1%
8112
 
< 0.1%
9329
 
0.1%
10336
 
0.1%
ValueCountFrequency (%)
44764
 
< 0.1%
4301
 
< 0.1%
4272
 
< 0.1%
416106
 
< 0.1%
41555
 
< 0.1%
41452
 
< 0.1%
408190
 
0.1%
40750
 
< 0.1%
406833
0.3%
4051137
0.4%

KARDEX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4617
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.69362964
Minimum0
Maximum100
Zeros3870
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:21.055484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60.28
Q177.419
median83.448
Q388.818
95-th percentile93.955
Maximum100
Range100
Interquartile range (IQR)11.399

Descriptive statistics

Standard deviation14.14541812
Coefficient of variation (CV)0.1752978294
Kurtosis14.9789562
Mean80.69362964
Median Absolute Deviation (MAD)5.612
Skewness-3.291546536
Sum20811532.63
Variance200.0928539
MonotonicityNot monotonic
2021-12-12T21:32:21.184119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03870
 
1.5%
91568
 
0.2%
81519
 
0.2%
85.091479
 
0.2%
88439
 
0.2%
78435
 
0.2%
81.818413
 
0.2%
83.018407
 
0.2%
85390
 
0.2%
79380
 
0.1%
Other values (4607)250008
96.9%
ValueCountFrequency (%)
03870
1.5%
2.14311
 
< 0.1%
512
 
< 0.1%
5.8336
 
< 0.1%
8.33324
 
< 0.1%
8.69
 
< 0.1%
1025
 
< 0.1%
10.8336
 
< 0.1%
1123
 
< 0.1%
13.8336
 
< 0.1%
ValueCountFrequency (%)
10045
< 0.1%
99.96260
< 0.1%
99.7529
< 0.1%
99.73745
< 0.1%
99.54
 
< 0.1%
99.47316
 
< 0.1%
99.22658
< 0.1%
99.15420
 
< 0.1%
99.05345
< 0.1%
9936
< 0.1%

PERIODO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct967
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.47300078
Minimum0
Maximum100
Zeros82563
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:21.313494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median73.667
Q391.8
95-th percentile98.333
Maximum100
Range100
Interquartile range (IQR)91.8

Descriptive statistics

Standard deviation41.21387771
Coefficient of variation (CV)0.7707418157
Kurtosis-1.677872297
Mean53.47300078
Median Absolute Deviation (MAD)23.583
Skewness-0.3352328207
Sum13791114.69
Variance1698.583716
MonotonicityNot monotonic
2021-12-12T21:32:21.588248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
082563
32.0%
1006961
 
2.7%
954835
 
1.9%
904091
 
1.6%
504079
 
1.6%
852537
 
1.0%
802233
 
0.9%
921903
 
0.7%
92.51827
 
0.7%
97.51764
 
0.7%
Other values (957)145115
56.3%
ValueCountFrequency (%)
082563
32.0%
166
 
< 0.1%
259
 
< 0.1%
2.5104
 
< 0.1%
2.813
 
< 0.1%
311
 
< 0.1%
3.2536
 
< 0.1%
3.33312
 
< 0.1%
3.522
 
< 0.1%
3.66760
 
< 0.1%
ValueCountFrequency (%)
1006961
2.7%
99.833115
 
< 0.1%
99.71432
 
< 0.1%
99.667139
 
0.1%
99.6172
 
0.1%
99.57189
 
< 0.1%
99.5163
 
0.1%
99.42968
 
< 0.1%
99.333139
 
0.1%
99.286261
 
0.1%

INICIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2127.370082
Minimum792
Maximum2212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:21.724072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum792
5-th percentile2062
Q12102
median2132
Q32172
95-th percentile2202
Maximum2212
Range1420
Interquartile range (IQR)70

Descriptive statistics

Standard deviation87.36810782
Coefficient of variation (CV)0.04106859854
Kurtosis132.3536017
Mean2127.370082
Median Absolute Deviation (MAD)30
Skewness-10.04025996
Sum548665763
Variance7633.186264
MonotonicityNot monotonic
2021-12-12T21:32:21.857055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211225173
 
9.8%
210224054
 
9.3%
216217648
 
6.8%
209217511
 
6.8%
215217174
 
6.7%
214216934
 
6.6%
218216648
 
6.5%
217216258
 
6.3%
213215898
 
6.2%
219215603
 
6.0%
Other values (53)75007
29.1%
ValueCountFrequency (%)
79221
 
< 0.1%
8212
 
< 0.1%
86213
 
< 0.1%
8715
 
< 0.1%
87227
 
< 0.1%
8821
 
< 0.1%
9021
 
< 0.1%
91117
 
< 0.1%
92274
< 0.1%
9312
 
< 0.1%
ValueCountFrequency (%)
22124845
 
1.9%
221172
 
< 0.1%
220212100
4.7%
2201129
 
0.1%
219215603
6.0%
219177
 
< 0.1%
218216648
6.5%
218151
 
< 0.1%
217216258
6.3%
216217648
6.8%

ULTIMO CICLO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2138.87278
Minimum0
Maximum2212
Zeros4628
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:21.988856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2111
Q12151
median2182
Q32211
95-th percentile2212
Maximum2212
Range2212
Interquartile range (IQR)60

Descriptive statistics

Standard deviation291.6023209
Coefficient of variation (CV)0.1363345794
Kurtosis49.00347988
Mean2138.87278
Median Absolute Deviation (MAD)29
Skewness-7.089689394
Sum551632401
Variance85031.91355
MonotonicityNot monotonic
2021-12-12T21:32:22.101178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
221165864
25.5%
220220264
 
7.9%
221218922
 
7.3%
216211021
 
4.3%
219210890
 
4.2%
218210795
 
4.2%
217210285
 
4.0%
21619096
 
3.5%
21318799
 
3.4%
21418627
 
3.3%
Other values (31)83345
32.3%
ValueCountFrequency (%)
04628
1.8%
9115
 
< 0.1%
9614
 
< 0.1%
97127
 
< 0.1%
98224
 
< 0.1%
201210
 
< 0.1%
20321
 
< 0.1%
204115
 
< 0.1%
204227
 
< 0.1%
205120
 
< 0.1%
ValueCountFrequency (%)
221218922
 
7.3%
221165864
25.5%
220220264
 
7.9%
22014468
 
1.7%
219210890
 
4.2%
21917123
 
2.8%
218210795
 
4.2%
21818079
 
3.1%
217210285
 
4.0%
21716337
 
2.5%

MATERIA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6724.257398
Minimum6
Maximum9939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:22.253523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile121
Q16981
median7440
Q38930
95-th percentile9232
Maximum9939
Range9933
Interquartile range (IQR)1949

Descriptive statistics

Standard deviation2874.393289
Coefficient of variation (CV)0.427466279
Kurtosis0.7514554508
Mean6724.257398
Median Absolute Deviation (MAD)1257
Skewness-1.401723609
Sum1734239777
Variance8262136.778
MonotonicityNot monotonic
2021-12-12T21:32:22.422370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1236469
 
2.5%
1246029
 
2.3%
1205962
 
2.3%
1215784
 
2.2%
1194492
 
1.7%
69814303
 
1.7%
69804213
 
1.6%
69853803
 
1.5%
69993718
 
1.4%
69843710
 
1.4%
Other values (1248)209425
81.2%
ValueCountFrequency (%)
64
 
< 0.1%
135
 
< 0.1%
8114
 
< 0.1%
1194492
1.7%
1205962
2.3%
1215784
2.2%
1236469
2.5%
1246029
2.3%
1272
 
< 0.1%
1282
 
< 0.1%
ValueCountFrequency (%)
99392
 
< 0.1%
99372
 
< 0.1%
99362
 
< 0.1%
99354
 
< 0.1%
99344
 
< 0.1%
99332
 
< 0.1%
99327
< 0.1%
99312
 
< 0.1%
993012
< 0.1%
99264
 
< 0.1%

NOMBRE MATERIA
Categorical

HIGH CARDINALITY

Distinct1108
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
NUEVAS TECNOLOGÍAS DE LA INFORMACIÓN Y LA COMUNICACIÓN
 
6469
ÉTICA Y DESARROLLO PROFESIONAL
 
6029
ESTRATEGIAS PARA APRENDER A APRENDER
 
5962
CARACTERÍSTICAS DE LA SOCIEDAD ACTUAL
 
5784
ACTIVIDADES CULTURALES Y DEPORTIVAS
 
4492
Other values (1103)
229172 

Length

Max length86
Median length23
Mean length25.2170619
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowMETODOLOGÍA DE LA INVESTIGACIÓN
2nd rowPRINCIPIOS DE DERECHO
3rd rowCONTABILIDAD I
4th rowADMINISTRACIÓN I
5th rowMATEMÁTICAS I

Common Values

ValueCountFrequency (%)
NUEVAS TECNOLOGÍAS DE LA INFORMACIÓN Y LA COMUNICACIÓN6469
 
2.5%
ÉTICA Y DESARROLLO PROFESIONAL6029
 
2.3%
ESTRATEGIAS PARA APRENDER A APRENDER5962
 
2.3%
CARACTERÍSTICAS DE LA SOCIEDAD ACTUAL5784
 
2.2%
ACTIVIDADES CULTURALES Y DEPORTIVAS4492
 
1.7%
CONTABILIDAD I4303
 
1.7%
MATEMÁTICAS I4213
 
1.6%
CONTABILIDAD II3803
 
1.5%
ADMINISTRACIÓN I3719
 
1.4%
MATEMÁTICAS II3710
 
1.4%
Other values (1098)209424
81.2%

Length

2021-12-12T21:32:22.656774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de79220
 
9.0%
i47089
 
5.3%
y45501
 
5.2%
enfermería38447
 
4.4%
la35845
 
4.1%
ii32912
 
3.7%
administración28903
 
3.3%
en21674
 
2.5%
contabilidad19194
 
2.2%
a16524
 
1.9%
Other values (836)516535
58.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HORAS TEORIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.306927276
Minimum0
Maximum6
Zeros49422
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:22.775321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.676777572
Coefficient of variation (CV)0.7268445735
Kurtosis-0.9409123765
Mean2.306927276
Median Absolute Deviation (MAD)1
Skewness0.2294248676
Sum594975
Variance2.811583027
MonotonicityNot monotonic
2021-12-12T21:32:22.877717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
364905
25.2%
049422
19.2%
246801
18.1%
141318
16.0%
537288
14.5%
415072
 
5.8%
63102
 
1.2%
ValueCountFrequency (%)
049422
19.2%
141318
16.0%
246801
18.1%
364905
25.2%
415072
 
5.8%
537288
14.5%
63102
 
1.2%
ValueCountFrequency (%)
63102
 
1.2%
537288
14.5%
415072
 
5.8%
364905
25.2%
246801
18.1%
141318
16.0%
049422
19.2%

HORAS LABORATORIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.618445337
Minimum0
Maximum20
Zeros64815
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:23.049851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile10
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.191691074
Coefficient of variation (CV)1.21892599
Kurtosis11.54920316
Mean2.618445337
Median Absolute Deviation (MAD)1
Skewness3.092055788
Sum675318
Variance10.18689191
MonotonicityNot monotonic
2021-12-12T21:32:23.162551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
369006
26.8%
064815
25.1%
262805
24.4%
424401
 
9.5%
117732
 
6.9%
153689
 
1.4%
102989
 
1.2%
82957
 
1.1%
122679
 
1.0%
52227
 
0.9%
Other values (6)4608
 
1.8%
ValueCountFrequency (%)
064815
25.1%
117732
 
6.9%
262805
24.4%
369006
26.8%
424401
 
9.5%
52227
 
0.9%
6907
 
0.4%
728
 
< 0.1%
82957
 
1.1%
98
 
< 0.1%
ValueCountFrequency (%)
201753
0.7%
194
 
< 0.1%
181908
0.7%
153689
1.4%
122679
1.0%
102989
1.2%
98
 
< 0.1%
82957
1.1%
728
 
< 0.1%
6907
 
0.4%

GRUPO
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size8.8 MiB

TIPO INSCRIPCION
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
I
231444 
F
 
9787
A
 
7462
C
 
6065
DP
 
1152
Other values (6)
 
1998

Length

Max length2
Median length1
Mean length1.007192487
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCP
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
I231444
89.7%
F9787
 
3.8%
A7462
 
2.9%
C6065
 
2.4%
DP1152
 
0.4%
E918
 
0.4%
CP516
 
0.2%
M315
 
0.1%
EP186
 
0.1%
P62
 
< 0.1%

Length

2021-12-12T21:32:23.328275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i231444
89.7%
f9787
 
3.8%
a7462
 
2.9%
c6065
 
2.4%
dp1152
 
0.4%
e918
 
0.4%
cp516
 
0.2%
m315
 
0.1%
ep186
 
0.1%
p62
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ESTATUS MATERIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
A
197722 
R
25622 
BV
 
17060
C
 
15350
BE
 
1049
Other values (4)
 
1105

Length

Max length3
Median length1
Mean length1.078357399
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A197722
76.7%
R25622
 
9.9%
BV17060
 
6.6%
C15350
 
6.0%
BE1049
 
0.4%
B74621
 
0.2%
B38365
 
0.1%
BG110
 
< 0.1%
B6B9
 
< 0.1%

Length

2021-12-12T21:32:23.442260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:23.543069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a197722
76.7%
r25622
 
9.9%
bv17060
 
6.6%
c15350
 
6.0%
be1049
 
0.4%
b74621
 
0.2%
b38365
 
0.1%
bg110
 
< 0.1%
b6b9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CALIFICACION ORDINARIO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.78413233
Minimum0
Maximum100
Zeros45729
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:23.712218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median80
Q392
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)42

Descriptive statistics

Standard deviation35.16977962
Coefficient of variation (CV)0.5346240556
Kurtosis-0.4872278837
Mean65.78413233
Median Absolute Deviation (MAD)15
Skewness-1.004226813
Sum16966254
Variance1236.913398
MonotonicityNot monotonic
2021-12-12T21:32:23.867365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045729
17.7%
10032482
 
12.6%
9019212
 
7.4%
8014935
 
5.8%
9512534
 
4.9%
8511456
 
4.4%
509991
 
3.9%
708949
 
3.5%
607863
 
3.0%
757156
 
2.8%
Other values (91)87601
34.0%
ValueCountFrequency (%)
045729
17.7%
1357
 
0.1%
2677
 
0.3%
3416
 
0.2%
439
 
< 0.1%
562
 
< 0.1%
655
 
< 0.1%
739
 
< 0.1%
866
 
< 0.1%
949
 
< 0.1%
ValueCountFrequency (%)
10032482
12.6%
99735
 
0.3%
983539
 
1.4%
972648
 
1.0%
963059
 
1.2%
9512534
 
4.9%
943155
 
1.2%
933617
 
1.4%
924240
 
1.6%
912740
 
1.1%

CALIFICACION EXTRAORDINARIO
Real number (ℝ≥0)

ZEROS

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.502403958
Minimum0
Maximum100
Zeros251653
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:24.153616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.861774961
Coefficient of variation (CV)6.563996925
Kurtosis45.73823747
Mean1.502403958
Median Absolute Deviation (MAD)0
Skewness6.752553957
Sum387482
Variance97.25460538
MonotonicityNot monotonic
2021-12-12T21:32:24.301117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0251653
97.6%
601526
 
0.6%
50768
 
0.3%
70582
 
0.2%
80382
 
0.1%
65374
 
0.1%
75282
 
0.1%
55243
 
0.1%
40171
 
0.1%
90151
 
0.1%
Other values (89)1776
 
0.7%
ValueCountFrequency (%)
0251653
97.6%
11
 
< 0.1%
21
 
< 0.1%
32
 
< 0.1%
53
 
< 0.1%
64
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
1026
 
< 0.1%
ValueCountFrequency (%)
10097
< 0.1%
995
 
< 0.1%
982
 
< 0.1%
972
 
< 0.1%
965
 
< 0.1%
9543
< 0.1%
946
 
< 0.1%
9312
 
< 0.1%
9211
 
< 0.1%
915
 
< 0.1%

NUMERO INSCRIPCIONES
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9741458194
Minimum-1
Maximum5
Zeros28204
Zeros (%)10.9%
Negative3
Negative (%)< 0.1%
Memory size2.0 MiB
2021-12-12T21:32:24.419775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum5
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4676924501
Coefficient of variation (CV)0.480105176
Kurtosis5.065494305
Mean0.9741458194
Median Absolute Deviation (MAD)0
Skewness0.6905712267
Sum251240
Variance0.2187362279
MonotonicityNot monotonic
2021-12-12T21:32:24.515326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1211567
82.0%
028204
 
10.9%
214731
 
5.7%
33399
 
1.3%
-13
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
-13
 
< 0.1%
028204
 
10.9%
1211567
82.0%
214731
 
5.7%
33399
 
1.3%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
43
 
< 0.1%
33399
 
1.3%
214731
 
5.7%
1211567
82.0%
028204
 
10.9%
-13
 
< 0.1%

NUMERO REP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0
216349 
1
33163 
2
 
6997
3
 
1398
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0216349
83.9%
133163
 
12.9%
26997
 
2.7%
31398
 
0.5%
41
 
< 0.1%

Length

2021-12-12T21:32:24.644088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:24.708807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0216349
83.9%
133163
 
12.9%
26997
 
2.7%
31398
 
0.5%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NUMERO BAJAS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0
229848 
1
24651 
2
 
3409

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0229848
89.1%
124651
 
9.6%
23409
 
1.3%

Length

2021-12-12T21:32:24.829353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-12T21:32:24.903244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0229848
89.1%
124651
 
9.6%
23409
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CICLO MATERIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2153.751237
Minimum2092
Maximum2212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:24.983691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2092
5-th percentile2101
Q12121
median2152
Q32191
95-th percentile2211
Maximum2212
Range120
Interquartile range (IQR)70

Descriptive statistics

Standard deviation37.10267215
Coefficient of variation (CV)0.01722699981
Kurtosis-1.257760281
Mean2153.751237
Median Absolute Deviation (MAD)31
Skewness0.02176557996
Sum555469674
Variance1376.608281
MonotonicityNot monotonic
2021-12-12T21:32:25.105052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
220212639
 
4.9%
210212464
 
4.8%
221212338
 
4.8%
211212081
 
4.7%
221111446
 
4.4%
212211067
 
4.3%
209210913
 
4.2%
219210802
 
4.2%
212110778
 
4.2%
214210512
 
4.1%
Other values (15)142868
55.4%
ValueCountFrequency (%)
209210913
4.2%
21019143
3.5%
210212464
4.8%
21119910
3.8%
211212081
4.7%
212110778
4.2%
212211067
4.3%
213110361
4.0%
213210363
4.0%
21418823
3.4%
ValueCountFrequency (%)
221212338
4.8%
221111446
4.4%
220212639
4.9%
22019401
3.6%
219210802
4.2%
21918838
3.4%
218210005
3.9%
21818811
3.4%
21729842
3.8%
21718892
3.4%

CLAVE MAESTRO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2076
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20887.66259
Minimum0
Maximum97244
Zeros47427
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:25.240399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115981
median24342
Q329286
95-th percentile32239
Maximum97244
Range97244
Interquartile range (IQR)13305

Descriptive statistics

Standard deviation11427.6444
Coefficient of variation (CV)0.5471002013
Kurtosis0.3171627595
Mean20887.66259
Median Absolute Deviation (MAD)5283
Skewness-0.7968120508
Sum5387095282
Variance130591056.6
MonotonicityNot monotonic
2021-12-12T21:32:25.393454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047427
 
18.4%
239083454
 
1.3%
222232832
 
1.1%
243412828
 
1.1%
89452222
 
0.9%
236742189
 
0.8%
117051992
 
0.8%
277201894
 
0.7%
268381872
 
0.7%
298681844
 
0.7%
Other values (2066)189354
73.4%
ValueCountFrequency (%)
047427
18.4%
5130357
 
0.1%
5230556
 
0.2%
52907
 
< 0.1%
53006
 
< 0.1%
53105
 
< 0.1%
5322577
 
0.2%
53408
 
< 0.1%
534475
 
< 0.1%
55212
 
< 0.1%
ValueCountFrequency (%)
97244103
< 0.1%
971042
 
< 0.1%
970156
 
< 0.1%
965917
 
< 0.1%
819346
 
< 0.1%
800662
 
< 0.1%
800126
 
< 0.1%
800026
 
< 0.1%
349561
 
< 0.1%
349372
 
< 0.1%

CLAVE MAESTRO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2107
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22718.81773
Minimum0
Maximum97244
Zeros31249
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:25.524146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121723
median25715
Q329868
95-th percentile33003
Maximum97244
Range97244
Interquartile range (IQR)8145

Descriptive statistics

Standard deviation10282.01211
Coefficient of variation (CV)0.4525769007
Kurtosis1.580645605
Mean22718.81773
Median Absolute Deviation (MAD)4152
Skewness-1.052909237
Sum5859364842
Variance105719773.1
MonotonicityNot monotonic
2021-12-12T21:32:25.654719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031249
 
12.1%
239083722
 
1.4%
222233347
 
1.3%
243413182
 
1.2%
89452622
 
1.0%
236742246
 
0.9%
298682216
 
0.9%
277202066
 
0.8%
117052058
 
0.8%
268382007
 
0.8%
Other values (2097)203193
78.8%
ValueCountFrequency (%)
031249
12.1%
5130370
 
0.1%
5230561
 
0.2%
52905
 
< 0.1%
53004
 
< 0.1%
53103
 
< 0.1%
5322687
 
0.3%
53404
 
< 0.1%
534416
 
< 0.1%
55212
 
< 0.1%
ValueCountFrequency (%)
97244103
< 0.1%
971041
 
< 0.1%
970155
 
< 0.1%
965917
 
< 0.1%
819343
 
< 0.1%
800662
 
< 0.1%
800152
 
< 0.1%
800125
 
< 0.1%
800023
 
< 0.1%
3519180
< 0.1%
Distinct2913
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2009-08-11 00:00:00
Maximum2021-11-19 00:00:00
2021-12-12T21:32:25.786767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:25.928604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TIPO MOVIMIENTO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean847.9993292
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2021-12-12T21:32:26.058411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q11000
median1000
Q31000
95-th percentile1000
Maximum1000
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation357.5095189
Coefficient of variation (CV)0.4215917472
Kurtosis1.713512555
Mean847.9993292
Median Absolute Deviation (MAD)0
Skewness-1.926969976
Sum218705811
Variance127813.0561
MonotonicityNot monotonic
2021-12-12T21:32:26.151319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1000218423
84.7%
425400
 
9.8%
169856
 
3.8%
12053
 
0.8%
7643
 
0.2%
2542
 
0.2%
24328
 
0.1%
9267
 
0.1%
13238
 
0.1%
11103
 
< 0.1%
ValueCountFrequency (%)
12053
 
0.8%
2542
 
0.2%
425400
9.8%
7643
 
0.2%
9267
 
0.1%
11103
 
< 0.1%
13238
 
0.1%
169856
 
3.8%
24328
 
0.1%
2555
 
< 0.1%
ValueCountFrequency (%)
1000218423
84.7%
2555
 
< 0.1%
24328
 
0.1%
169856
 
3.8%
13238
 
0.1%
11103
 
< 0.1%
9267
 
0.1%
7643
 
0.2%
425400
 
9.8%
2542
 
0.2%

Interactions

2021-12-12T21:32:09.646208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:30:57.813301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:02.188307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:06.279120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:10.859749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:14.747406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:18.906201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:23.705896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:28.107170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:32.238946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:36.608962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:40.511618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:44.802908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:48.667677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:52.208985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:56.415984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:00.304877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:05.165264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:09.849954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:30:58.080495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:02.403406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:06.531946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:11.057021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:14.973282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:19.108173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:23.936726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:28.304421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:32.471246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:36.836032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:40.714888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:45.003166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:48.851524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:52.402900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:56.612992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:00.517798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:05.403429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:10.089023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:30:58.335124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:02.656778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:06.807639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:11.277857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:15.290268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:19.435373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:24.180353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:28.660579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-12-12T21:31:47.553620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:51.240245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:55.271867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:59.233580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:03.936560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:08.335653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:12.980905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:01.293059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:05.343263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:09.899050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:13.790779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:18.034362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:22.723166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:27.226155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:31.283338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:35.662904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:39.663415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:43.888149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:47.755948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:51.425095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:55.524420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:59.452682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:04.195291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:08.572100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:13.214909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:01.516845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:05.565693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:10.155239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:14.137439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:18.245502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:22.988661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:27.464992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:31.559991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:35.903223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:39.873760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:44.104998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:47.952503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:51.625005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:55.755225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:59.671830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:04.432568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:08.798405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:13.440052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:01.739346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:05.796851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:10.402414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:14.329605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:18.469738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:23.239711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:27.676295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:31.806661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:36.137222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:40.089044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:44.357958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:48.153638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:51.818696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:55.978300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:59.897658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:04.676485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:09.036703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:13.674171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:01.966147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:06.024441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:10.641556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:14.533662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:18.692329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:23.473968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:27.892332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:32.023437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:36.382369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:40.288352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:44.573153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:48.352914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:52.014483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:31:56.192304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:00.102934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:04.919936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-12T21:32:09.280831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-12-12T21:32:26.406684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-12T21:32:26.702042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-12T21:32:27.041859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-12T21:32:27.376186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-12T21:32:27.587741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-12T21:32:14.229593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-12T21:32:16.563523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexDIVISIONNOMBRE DEPARTAMENTOEXPEDIENTEPROGRAMAPLANCREDITOS PASANTEESTATUSTIPOAVANCEKARDEXPERIODOINICIOULTIMO CICLOMATERIANOMBRE MATERIAHORAS TEORIAHORAS LABORATORIOGRUPOTIPO INSCRIPCIONESTATUS MATERIACALIFICACION ORDINARIOCALIFICACION EXTRAORDINARIONUMERO INSCRIPCIONESNUMERO REPNUMERO BAJASCICLO MATERIACLAVE MAESTROCLAVE MAESTRO2FECHA CAPTURATIPO MOVIMIENTO
00DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221327457METODOLOGÍA DE LA INVESTIGACIÓN130CPA8500002131002013-04-291000
11DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326987PRINCIPIOS DE DERECHO500CA6200002131002013-04-291000
22DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326981CONTABILIDAD I230CA6100002131002013-04-291000
33DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326999ADMINISTRACIÓN I500CA7900002131002013-04-291000
44DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326980MATEMÁTICAS I320CA8100002131002013-04-291000
55DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326986MICROECONOMÍA I500CA8000002131002013-04-291000
66DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221327430CONTABILIDAD III140CA7000002131002013-04-291000
77DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326997MACROECONOMÍA I500CA6500002131002013-04-291000
88DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221327436CALIDAD EN LAS ORGANIZACIONES210CPA9000002131002013-04-291000
99DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN7921124LICENCIATURA EN ADMINISTRACIÓN2042373I40R8572.8180.079221326985CONTABILIDAD II140CA6000002131002013-04-291000

Last rows

df_indexDIVISIONNOMBRE DEPARTAMENTOEXPEDIENTEPROGRAMAPLANCREDITOS PASANTEESTATUSTIPOAVANCEKARDEXPERIODOINICIOULTIMO CICLOMATERIANOMBRE MATERIAHORAS TEORIAHORAS LABORATORIOGRUPOTIPO INSCRIPCIONESTATUS MATERIACALIFICACION ORDINARIOCALIFICACION EXTRAORDINARIONUMERO INSCRIPCIONESNUMERO REPNUMERO BAJASCICLO MATERIACLAVE MAESTROCLAVE MAESTRO2FECHA CAPTURATIPO MOVIMIENTO
25789839583DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221216137LICENCIATURA EN ADMINISTRACIÓN2042373AR00.000.022120124ÉTICA Y DESARROLLO PROFESIONAL0332IC0010022120214072021-05-242
25789939584DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221216137LICENCIATURA EN ADMINISTRACIÓN2042373AR00.000.0221206980MATEMÁTICAS I329IC0010022120252642021-05-242
25790039585DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221216137LICENCIATURA EN ADMINISTRACIÓN2042373AR00.000.0221206981CONTABILIDAD I236IC0010022120185802021-05-242
25790139586DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221216137LICENCIATURA EN ADMINISTRACIÓN2042373AR00.000.0221206987PRINCIPIOS DE DERECHO506IC0010022120281482021-05-242
25790239587DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221216137LICENCIATURA EN ADMINISTRACIÓN2042373AR00.000.0221206999ADMINISTRACIÓN I507IC0010022120277082021-05-242
25790339588DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221218316LICENCIATURA EN ADMINISTRACIÓN2042373AR1291.750.022120121CARACTERÍSTICAS DE LA SOCIEDAD ACTUAL0332IC0010022120227982021-05-207
25790439589DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221218316LICENCIATURA EN ADMINISTRACIÓN2042373AR1291.750.022120121CARACTERÍSTICAS DE LA SOCIEDAD ACTUAL030CPA9201002212002021-08-317
25790539590DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221218316LICENCIATURA EN ADMINISTRACIÓN2042373AR1291.750.022120124ÉTICA Y DESARROLLO PROFESIONAL0327IC0010022120281482021-05-207
25790639591DIVISIÓN DE CIENCIAS ECONÓMICAS Y ADMINISTRATIVASDEPARTAMENTO DE ADMINISTRACIÓN221218316LICENCIATURA EN ADMINISTRACIÓN2042373AR1291.750.022120124ÉTICA Y DESARROLLO PROFESIONAL030CPA9001002212002021-08-317
25790739592DIVISIÓN DE CIENCIAS BIOLÓGICAS Y DE LA SALUDDEPARTAMENTO DE ENFERMERÍA221230231LICENCIATURA EN ENFERMERÍA2172398AR00.000.0221203945SOCIOLOGÍA Y SALUD211ABE0000022120243422021-08-241